On pseudolinear functions

In this paper the structure of a smooth pseudolinear function is investigated and the general form of the gradient is given explicitly.     

Convexity of the Implicit Lagrangian

The implicit Lagrangian has attracted much attention recently because of its utility in reformulating complementarity and variational inequality problems as unconstrained minimization problems. It was first proposed by Mangasarian and Solodov as a merit function for the nonlinear complementarity problem (Ref. 1). Three open problems were also raised in the same paper. This paper addresses, among other issues, one of these problems by giving the properties of the implicit Lagrangian and establishing its convexity under appropriate assumptions.     

The simplex method as a global optimizer: A c-programming perspective

In this paper we give a brief account of the important role that the conventional simplex method of linear programming can play in global optimization, focusing on its collaboration with composite concave programming techniques. In particular, we demonstrate how rich and powerful the c-programming format is in cases where its parametric problem is a standard linear programming problem.     

几何凹函数的一个重要性质及其应用

设a0,定义在[a,b]上的函数f在(a,b)上是几何凹函数,将证明F(x)=∫xaf(t)dt在(a,b)也为几何凹函数,并用实例说明其应用.     

关于几何凸函数的Hadamard型不等式

建立了一个关于几何凸函数的 Hadamard型不等式 .     

Bregman Proximal Relaxation of Large-Scale 0–1 Problems

We apply a recent extension of the Bregman proximal method for convex programming to LP relaxations of 0–1 problems. We allow inexact subproblem solutions obtained via dual ascent, increasing their accuracy successively to retain global convergence. Our framework is applied to relaxations of large-scale set covering problems that arise in airline crew scheduling. Approximate relaxed solutions are used to construct primal feasible solutions via a randomized heuristic. Encouraging preliminary experience is reported.     

An application of Jack-Fukui-Sakaguchi Lemma

We present some applications of Jack-Fukui-Sakaguchi Lemma which become sufficient criteria for a function to be in the class of strongly starlike, strongly close-to-convex or in the other classes.     

Convergence of duality bound method in partly convex programming

We discuss the convergence of a decomposition branch-and-bound algorithm using Lagrangian duality for partly convex programs in the general form. It is shown that this decomposition algorithm has all convergence properties as any known branch-and-bound algorithm in global optimization under usual assumptions. Thus, some strict assumptions discussed in the literature are avoidable.     

Merit functions for semi-definite complemetarity problems

Merit functions such as the gap function, the regularized gap function, the implicit Lagrangian, and the norm squared of the Fischer-Burmeister function have played an important role in the solution of complementarity problems defined over the cone of nonnegative real vectors. We study the extension of these merit functions to complementarity problems defined over the cone of block-diagonal symmetric positive semi-definite real matrices. The extension suggests new solution methods for the latter problems. This research is supported by National Science Foundation Grant CCR-9311621.     

On biconjugates of infimal functions

We deliver formulae for the biconjugate functions of some infimal functions that hold provided the fulfilment of weak regularity conditions of both closedness and interiority types. As special cases, we obtain biconjugates of infimal convolutions of finitely many functions, of optimal value functions of both constrained and unconstrained optimization problems as well as of marginal functions associated with multifunctions (that can be, for instance, convex processes) and some given functions. Moreover, we rediscover or extend different results on biconjugate functions from the literature.     

Solving a class of multiplicative programming problems via c-programming

In this note we show that many classes of global optimization problems can be treated most satisfactorily by classical optimization theory and conventional algorithms. We focus on the class of problems involving the minimization of the product of several convex functions on a convex set which was studied recently by Kunoet al. [3]. It is shown that these problems are typical composite concave programming problems and thus can be handled elegantly by c-programming [4]–[8] and its techniques.     

N元指数和对数平均的凸性及几何凸性

讨论了n元指数平均和对数平均的凸性、S-凸性、几何凸性及S-几何凸性，证明了：（1）n元指数平均是S-凹的和S-几何凸的；（2）n元第一对数平均是S-凹的；（3）n元第二对数平均是凹的和几何凸的．最后提出了二个悬而未决的问题．     

带有极值函数的多目标双层规划问题的对偶

For a multiobjective bilevel programming problem(P) with an extremal-value function,its dual problem is constructed by using the Fenchel-Moreau conjugate of the functions involved.Under some convexity and monotonicity assumptions,the weak and strong duality assertions are obtained.     

An example of bad convex function

An example of a convex function having the gradient at each point (x, 0, ..., 0),x>0, which does not converge, whenx tends to zero, is given.The author would like to thank Professor Giannessi for his help, which led to an improved version of this paper.     

关于半连续函数与凸函数的注记

在半连续前提下,给出凸函数和严格凸函数的不等式刻划．指出非空凸集上的半连续函数满足中间点凸性时,成为凸函数,满足中间点严格凸性时,成为严格凸函数．最后定义F—G广义凸函数和条件p1,p2等概念,列举若干满足条件p1,p2的数量函数和向量函数,并指出,对于F—G广义凸函数,在条件p1,p2及一定连续性条件下,可以得到类似结果．     

Convergence rate of a proximal multiplier algorithm for separable convex minimization

In this paper, we analyse the convergence rate of the proximal algorithm proposed by us in the article [A proximal multiplier method for separable convex minimization. Optimization. 2016; 65:501–537], which has been proposed to solve a separable convex minimization problem. We prove that, under mild assumptions, the primal-dual sequences of the algorithm converge linearly to the optimal solution for a class of proximal distances.     

Generalized fractional programming: Optimality and duality theory

The generalized fractional programming problem with a finite number of ratios in the objective is studied. Optimality and duality results are established, some with the help of an auxiliary problem and some directly. Convexity and stability of the auxiliary problem play a key role in the latter part of the paper.The authors are grateful to an unknown referee for suggesting the statement of Theorem 3.3.     

A subclass of parabolic starlike and uniformly convex functions

Let A be the class of analytic functions in the open unit disk U. A function f in A satisfying the normalization is said to be in the class SP_n if Dⁿf is a parabolic starlike function, where Dⁿ is a notation of the Salagean operator. In this paper, several basic properties and characteristics of the class SP_n are investigated. These include subordination, convolution properties, class-preserving integral operators, and Fekete-Szegö problems.     

Refinement of Lagrangian bounds in optimization problems

Lagrangian constraint relaxation and the corresponding bounds for the optimal value of an original optimization problem are examined. Techniques for the refinement of the classical Lagrangian bounds are investigated in the case where the complementary slackness conditions are not fulfilled because either the original formulation is nonconvex or the Lagrange multipliers are nonoptimal. Examples are given of integer and convex problems for which the modified bounds improve the classical Lagrangian bounds.